DAC Analytics Dashboard: Code Review and Future Plans

Current Implementation Overview

The current implementation is a Shiny dashboard that monitors Direct Air Capture (DAC) installations worldwide.

Key Features

• Data Management: Tracks 5 DAC installations with simulated operational data
• Visualization Components:
  • Interactive global installation map
  • Real-time CO₂ capture metrics
  • Performance comparisons across facilities
  • Environmental impact analysis
  • Cost efficiency tracking

# Create DAC installations data
dac_installations <- data.frame(
    name = c("Orca", "Mammoth", "Project Bison", "Squamish", "Arizona DAC"),
    company = c("Climeworks", "Climeworks", "CarbonCapture", "Carbon Engineering", "Carbon Capture Inc"),
    type = c("Commercial", "Commercial", "Commercial", "Pilot", "Research"),
    latitude = c(64.0297, 64.0297, 41.8240, 49.7016, 33.4484),
    longitude = c(-21.9877, -21.9877, -107.9511, -123.1558, -112.0740),
    capacity = c(4000, 36000, 5000, 1000, 3500),
    start_date = as.Date(c("2021-09-01", "2024-01-15", "2023-12-01", "2015-06-01", "2022-03-01")),
    technology = c("Solid Sorbent", "Solid Sorbent", "Solid Sorbent", "Liquid Solvent", "Membrane"),
    stringsAsFactors = FALSE
)

# Display current installations
dac_installations %>%
  select(name, technology, capacity) %>%
  arrange(desc(capacity)) %>%
  mutate(
    capacity = paste0(format(capacity, big.mark=","), " tons/year")
  ) %>%
  kable(col.names = c("Facility", "Technology Type", "Annual Capacity")) %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

Facility	Technology Type	Annual Capacity
Mammoth	Solid Sorbent	36,000 tons/year
Project Bison	Solid Sorbent	5,000 tons/year
Orca	Solid Sorbent	4,000 tons/year
Arizona DAC	Membrane	3,500 tons/year
Squamish	Liquid Solvent	1,000 tons/year

Current Limitations

1. Data Generation: Relies on simulated data
2. Static Installation Data: Fixed set of 5 DAC installations
3. Limited Error Handling
4. Basic Authentication
5. Limited Data Export

# Generate operational data function
generate_operational_data <- function(installations, days = 365) {
    dates <- seq(Sys.Date() - days, Sys.Date(), by = "day")
    operational_data <- expand.grid(
        installation_name = installations$name,
        date = dates,
        stringsAsFactors = FALSE
    ) %>%
    left_join(installations[, c("name", "capacity", "start_date")], 
              by = c("installation_name" = "name")) %>%
    filter(date >= start_date) %>%
    group_by(installation_name) %>%
    mutate(
        efficiency_factor = 0.8 + 0.15 * sin(2 * pi * as.numeric(format(date, "%j")) / 365),
        daily_capacity = capacity / 365,
        co2_captured = daily_capacity * efficiency_factor * (1 + rnorm(n(), 0, 0.05)),
        energy_used = co2_captured * (2.0 + rnorm(n(), 0, 0.2)),
        operating_cost = co2_captured * (400 + rnorm(n(), 0, 50)),
        temperature = 15 + 10 * sin(2 * pi * as.numeric(format(date, "%j")) / 365) + rnorm(n(), 0, 2),
        humidity = pmin(pmax(60 + rnorm(n(), 0, 10), 0), 100)
    ) %>%
    ungroup() %>%
    select(-efficiency_factor, -daily_capacity)
    
    return(operational_data)
}

# Generate the data
operational_data <- generate_operational_data(dac_installations)

Performance Analysis

# Temperature vs Efficiency Plot
plot_ly(operational_data, 
        x = ~temperature, 
        y = ~co2_captured/energy_used * 100,
        type = "scatter",
        mode = "markers",
        marker = list(
          color = "#4CAF50",
          size = 8,
          opacity = 0.6
        )) %>%
  add_lines(x = ~temperature, 
           y = ~fitted(loess(co2_captured/energy_used * 100 ~ temperature)),
           line = list(color = "#1B5E20")) %>%
  layout(
    title = "Temperature Effects on System Efficiency",
    xaxis = list(title = "Temperature (°C)"),
    yaxis = list(title = "Efficiency (%)")
  )

Future Projections

future_projections <- data.frame(
  Year = 2024:2030,
  Expected_Capacity = c(5000, 15000, 45000, 100000, 200000, 350000, 500000),
  Optimistic_Capacity = c(5000, 20000, 60000, 150000, 300000, 500000, 750000),
  Conservative_Capacity = c(5000, 12000, 30000, 70000, 150000, 250000, 350000)
)

plot_ly(future_projections, x = ~Year) %>%
  add_lines(y = ~Expected_Capacity, name = "Expected", line = list(color = "#4CAF50")) %>%
  add_lines(y = ~Optimistic_Capacity, name = "Optimistic", line = list(color = "#8BC34A")) %>%
  add_lines(y = ~Conservative_Capacity, name = "Conservative", line = list(color = "#1B5E20")) %>%
  layout(
    title = "Projected Annual Capture Capacity",
    yaxis = list(title = "Annual Capacity (tons)")
  )

Conclusion

The current implementation provides a solid foundation for DAC installation monitoring. Key areas for improvement include: - Data management and reliability - User experience and customization - Advanced analytics and predictions - System scalability and security